Hello! I'm Tyler, a financial data analytics professional with just over a decade of experience, primarily in the healthcare industry. I'm now transitioning from finance and accounting to focus solely on data science, where I aim to leverage my healthcare domain knowledge to drive data-driven solutions and insights.
- 💼 Professional Background: Extensive experience in healthcare, with a focus on financial model development, dashboard and report building, and data analytics
- 🎓 Education:
- IBM Data Science Professional Certificate (2024)
- Data Science in Stratified Healthcare and Precision Medicine Certificate (2024)
- B.S. in Finance (2013)
- 🌱 Currently Learning: Linear algebra and more of the theory behind ML to better inform my model development
- 🥅 Career Goals: As I further develop my data science toolbelt, I aim to serve in roles where I can utilize machine learning to improve public health and better understand the hidden social determinants (X) of health (y).
- 🛠️ Tools:
- Languages: SQL, Python
- Data Manipulation/Machine Learning: Pandas, NumPy, Scikit-Learn
- Visualization: Tableau, Matplotlib, Seaborn
- Others: Excel, Cuisinart ice cream maker
- 📫 Connect with me: LinkedIn
| Project | Skill Area | Description | Tools |
|---|---|---|---|
| Health Center Data ELT | ELT, database management, EDA, data querying | This project was motivated by the limitations of pre-built reports provided on the HRSA website. While high-level summaries were available, I wanted full access to the raw data to enable more granular analysis and custom reporting. To achieve this, I retrieved HRSA's annual UDS data, loaded it into a MySQL database, and performed data wrangling to ensure the dataset was clean and optimized for querying. By building my own SQL queries, I gained the flexibility to extract specific insights not readily available through standard reports. | SQL |
| Health Center Dashboards | Data visualization, dashboarding | My goal for this project was to transform my health center data into dynamic visualizations that offer deeper insights. To achieve this, I built two Tableau dashboards: one to visualize aggregated national-level data, providing a broad overview of HRSA's health center program, and a second to enable analysis of individual health centers through an interactive dropdown menu, allowing users to select and explore any specific health center’s data. | Tableau |
| Payer Mix Linear Regression | Linear regression, lasso regression, supervised machine learning | Accurately predicting payer mix percentages is important for health centers to optimize financial planning and resource allocation. To address this need, I developed linear regression models to predict payer mix distributions based on financial, clinical, and patient demographic data of health centers. | Python (Pandas, Scikit-Learn, Matplotlib, Seaborn) |
| NHANES k-means Clustering Analysis | k-means clustering, unsupervised machine learning | To explore patterns and groupings within health-related data, I applied k-means clustering to the NHANES dataset, focusing on key health features such as demographics, lifestyle factors, and clinical measurements. By clustering survey respondents, I was able to identify distinct population segments with similar health profiles within the NHANES dataset. | Python (Pandas, Scikit-Learn, Matplotlib, Seaborn) |